Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience

• URL: http://arxiv.org/abs/2402.07687v2
• Date: Wed, 21 Feb 2024 15:46:16 GMT
• Title: Privacy-Preserving Gaze Data Streaming in Immersive Interactive Virtual Reality: Robustness and User Experience
• Authors: Ethan Wilson, Azim Ibragimov, Michael J. Proulx, Sai Deep Tetali, Kevin Butler, Eakta Jain,
• Abstract summary: Eye tracking data, if exposed, can be used for re-identification attacks. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications.
• Score: 11.130411904676095
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions.

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